The Multiple Attribution Problem in Pay-Per-Conversion Advertising

  • Patrick Jordan
  • Mohammad Mahdian
  • Sergei Vassilvitskii
  • Erik Vee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6982)


In recent years the online advertising industry has witnessed a shift from the more traditional pay-per-impression model to the pay-per-click and more recently to the pay-per-conversion model. Such models require the ad allocation engine to translate the advertiser’s value per click/conversion to value per impression. This is often done through simple models that assume that each impression of the ad stochastically leads to a click/conversion independent of other impressions of the same ad, and therefore any click/conversion can be attributed to the last impression of the ad. However, this assumption is unrealistic, especially in the context of pay-per-conversion advertising, where it is well known in the marketing literature that the consumer often goes through a purchasing funnel before they make a purchase. Decisions to buy are rarely spontaneous, and therefore are not likely to be triggered by just the last ad impression. In this paper, we observe how the current method of attribution leads to inefficiency in the allocation mechanism. We develop a fairly general model to capture how a sequence of impressions can lead to a conversion, and solve the optimal ad allocation problem in this model. We will show that this allocation can be supplemented with a payment scheme to obtain a mechanism that is incentive compatible for the advertiser and fair for the publishers.


Optimal Allocation Bellman Equation Payment Scheme Uniform Price Conversion Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Aggarwal, G., Goel, A., Motwani, R.: Truthful auctions for pricing search keywords. In: Proceedings of the 7th ACM Conference on Electronic Commerce, EC 2006, pp. 1–7. ACM, New York (2006)Google Scholar
  2. 2.
    Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 44–54. ACM Press, New York (2006)CrossRefGoogle Scholar
  3. 3.
    Barry, T.E.: The development of the hierarchy of effects: An historical perspective. Current Issues and Research in Advertising 10, 251–295 (1987)Google Scholar
  4. 4.
    Danaher, P.J.: Advertising models. In: Wierenga, B. (ed.) Handbook of Marketing Decision Models. International Series in Operations Research and Management Science, vol. 121, pp. 81–106. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Dreller, J.: In the trenches SEM pre-click & post-click double feature: Conversion attribution and Q&A with analytics guru Eric Peterson. Search Engine Land (November 2008),
  6. 6.
    Dreller, J.: Research brief: Conversion attribution. Fuor Digital (December 2008),
  7. 7.
    Edelman, B., Ostrovsky, M., Schwarz, M.: Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywords. American Economic Review 97(1), 242–259 (2007)CrossRefGoogle Scholar
  8. 8.
    Fain, D.C., Pedersen, J.O.: Sponsored search: A brief history. Bulletin of the American Society for Information Science and Technology 32(2), 12–13 (2006)CrossRefGoogle Scholar
  9. 9.
    Farahat, A.: Privacy preserving frequency capping in internet banner advertising. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, pp. 1147–1148. ACM, New York (2009)Google Scholar
  10. 10.
    ClearSaleing Inc. Attribution management,
  11. 11.
    Lewis, R.: Where’s the ‘wear-out’? Working paper. Yahoo! Research (2011)Google Scholar
  12. 12.
    Mahdian, M., Tomak, K.: Pay-per-action model for online advertising. In: Deng, X., Graham, F.C. (eds.) WINE 2007. LNCS, vol. 4858, pp. 549–557. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Muthukrishnan, S.: Ad exchanges: Research issues. In: Leonardi, S. (ed.) WINE 2009. LNCS, vol. 5929, pp. 1–12. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    PricewaterhouseCoopers. IAB internet advertising revenue report: 2009 full-year results (2010),
  15. 15.
    Strong, E.K.: The Psychology of Selling Advertising. McGraw-Hill, New York (1925)Google Scholar
  16. 16.
    White, D.J.: Markov Decision Processes. Wiley, Chichester (1993)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Patrick Jordan
    • 1
  • Mohammad Mahdian
    • 1
  • Sergei Vassilvitskii
    • 1
  • Erik Vee
    • 1
  1. 1.Yahoo! ResearchSanta Clara

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